Situation-appropriate Investment of Cognitive Resources

The human brain is equipped with the ability to plan ahead, i.e. to mentally simulate the expected consequences of candidate actions to select the one with the most desirable expected long-term outcome. Insufficient planning can lead to maladaptive behaviour and may even be a contributory cause of important societal problems such as the depletion of natural resources or man-made climate change. Understanding the cognitive and neural mechanisms of forward planning and its regulation are therefore of great importance and could ultimately give us clues on how to better align our behaviour with long-term goals. Apart from its potential beneficial effects, planning is time-consuming and therefore associated with opportunity costs. It is assumed that the brain regulates the investment into planning based on a cost-benefit analysis, so that planning only takes place when the perceived benefits outweigh the costs. But how can the brain know in advance how beneficial or costly planning will be? One potential solution is that people learn from experience how valuable planning would be in a given situation. It is however largely unknown how the brain implements such learning, especially in environments with large state spaces. This dissertation tested the hypothesis that humans construct and use so-called control contexts to efficiently adjust the degree of planning to the demands of the current situation. Control contexts can be seen as abstract state representations, that conveniently cluster together situations with a similar demand for planning. Inferring context thus allows to prospectively adjust the control system to the learned demands of the global context. To test the control context hypothesis, two complex sequential decision making tasks were developed. Each of the two tasks had to fulfil two important criteria. First, the tasks should generate both situations in which planning had the potential to improve performance, as well as situations in which a simple strategy was sufficient. Second, the tasks had to feature rich state spaces requiring participants to compress their state representation for efficient regulation of planning. Participants’ planning was modelled using a parametrized dynamic programming solution to a Markov Decision Process, with parameters estimated via hierarchical Bayesian inference. The first study used a 15-step task in which participants had to make a series of decisions to achieve one or multiple goals. In this task, the computational costs of accurate forward planning increased exponentially with the length of the planning horizon. We therefore hypothesized that participants identify ‘distance from goal’ as the relevant contextual feature to guide their regulation of forward planning. As expected we found that participants predominantly relied on a simple heuristic when still far from the goal but progressively switched towards forward planning when the goal approached. In the second study participants had to sustainably invest a limited but replenishable energy resource, that was needed to accept offers, in order to accumulate a maximum number of points in the long run. The demand for planning varied across the different situations of the task, but due to the large number of possible situations (n = 448) it would be difficult for the participants to develop an expectation for each individual situation of how beneficial planning would be. We therefore hypothesized, that to regulate their forward planning participants used a compressed tasks representation, clustering together states with similar demands for planning. Consistent with this, reaction times (operationalising planning duration) increased with trial-by-trial value-conflict (operationalising approximate planning demand), but this increase was more pronounced in a context with generally high demand for planning. We further found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, but this increase was more pronounced in a context with generally high demand for planning as well. Taken together, the results suggest that the dACC integrates representations of planning demand on different levels of abstraction to regulate prospective information sampling in an efficient and situation-appropriate way. This dissertation provides novel insights into the question how humans adapt their planning to the demands of the current situation. The results are consistent with the view that the regulation of planning is based on an integrated signal of the expected costs and benefits of planning. Furthermore, the results of this dissertation provide evidence that the regulation of planning in environments with real-world complexity critically relies on the brain’s powerful ability to construct and use abstract hierarchical representations

Investigation of an emotional virtual human modelling method

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In order to simulate virtual humans more realistically and enable them life-like behaviours, several exploration research on emotion calculation, synthetic perception, and decision making process have been discussed. A series of sub-modules have been designed and simulation results have been presented with discussion. A visual based synthetic perception system has been proposed in this thesis, which allows virtual humans to detect the surrounding virtual environment through a collision-based synthetic vision system. It enables autonomous virtual humans to change their emotion states according to stimuli in real time. The synthetic perception system also allows virtual humans to remember limited information within their own First-in-first-out short-term virtual memory. The new emotion generation method includes a novel hierarchical emotion structure and a group of emotion calculation equations, which enables virtual humans to perform emotionally in real-time according to their internal and external factors. Emotion calculation equations used in this research were derived from psychologic emotion measurements. Virtual humans can utilise the information in virtual memory and emotion calculation equations to generate their own numerical emotion states within the hierarchical emotion structure. Those emotion states are important internal references for virtual humans to adopt appropriate behaviours and also key cues for their decision making. The work introduces a dynamic emotional motion database structure for virtual human modelling. When developing realistic virtual human behaviours, lots of subjects were motion-captured whilst performing emotional motions with or without intent. The captured motions were endowed to virtual characters and implemented in different virtual scenarios to help evoke and verify design ideas, possible consequences of simulation (such as fire evacuation). This work also introduced simple heuristics theory into decision making process in order to make the virtual human’s decision making more like real human. Emotion values are proposed as a group of the key cues for decision making under the simple heuristic structures. A data interface which connects the emotion calculation and the decision making structure together has also been designed for the simulation system

Computational Mechanisms of Social Influence During Adolescence

Adolescents are known for their propensity to take risks. They binge-drink (Spear, 2018), use drugs (Defoe et al., 2019), drive risky (Romer et al., 2014) and get into trouble with the law (Viner et al., 2011) more often than people in other developmental stages (Steinberg, 2018). However, a recent meta-analysis on adolescent risk-taking behaviour in controlled laboratory settings showed no evidence of an adolescent risk-taking peak (Defoe et al., 2015). Past research identified two major reasons for the discrepancy between adolescents’ real-life and laboratory risk-taking. First, adolescents are especially attentive to social signals (Blakemore & Mills 2014), and most adolescent risk-taking behaviour in real-life occurs in some kind of social context. Second, adolescents seem to be less inhibited by uncertainty than people in other developmental stages (Tymula et al., 2012; van den Bos & Hertwig, 2017). The risks that adolescents take in real-life are subject to much greater uncertainties than those portrayed in many laboratory experiments. In my dissertation, I investigate how adolescents' sensitivity to social signals and their uncertainties contribute to their propensity to take risks. Chapter 1 gives a broader literature overview that ends in pointing out how understanding the intersection between social susceptibility and uncertainty is crucial to understanding adolescent risk-taking. Chapter 2 points out that the developmental processes underlying adolescents’ social sensitivity remain poorly understood, despite extensive research and theorisation. I emphasise that while many theories assume different mechanisms behind adolescent social susceptibility, they are all consistent with a broad range of evidence from laboratory experiments. I thus propose a formal framework depicting these theories in mathematical equations that make precise predictions that can be evaluated against one another and show that doing so synthesises seemingly disparate results. Chapter 3 introduces a novel experimental paradigm that allows manipulating uncertainties and proposes a model that understands social influence as a learning process wherein social information is more impactful when people are more uncertain. This chapter shows that developmental differences in peoples’ uncertainties can partially explain developmental differences in social impact during risk-taking. Chapter 4 shows how social norms contribute to real-life risk-taking in the general population and provide evidence that adolescents overestimate the normative character of many risky behaviours. Chapter 5 points out that people in different developmental stages live in different environments that expose them to different risks. Using agent-based modelling, I show that an adolescent-peak in risky behaviour can, in combination with a tendency for exploration, be emergent form transitioning from a relatively safe childhood into a risky adult environment. Chapter 6 provides a summary and conclusion of the preceding chapters and points to future research questions that my dissertation opens up

Goals and information processing in human decisions

We do not make decisions in the void. Every day, we act in awareness of our context, adjusting our objectives according to the situations we find. Operating effectively under multiple goals is fundamental for appropriate learning and decision-making, and deficiencies in this capacity can be at the core of mental disorders such as anxiety, depression, or post-traumatic stress disorder. In this thesis, I present studies I conducted to investigate how goals impact different stages of the decision process, from simple perceptual choices to subjective value preferences. Previous studies have described how animals assess alternatives and integrate evidence to make decisions. Most of the time, the focus of this work has been on simplified scenarios with single goals. In this thesis, my experiments tackle the issue of how people adjust information processing in tasks that demand more than one objective. Through various manipulations of the behavioural goals, such as decision framing, I show that (i) attention and evidence accumulation, (ii) brain representations, and (iii) decision confidence were all affected by context changes. Using behavioural testing, computational models, and neuroimaging I show that goals have a crucial role in evidence integration and the allocation of visual attention. My findings indicate that brain patterns adapt to enhance goal-relevant information during learning and the valuation of alternatives. Finally, I report the presence of goal-dependent asymmetries in the generation of decision confidence, overweighting the evidence of the most-relevant option to fulfil the goal. In conclusion, I show how the entire process is highly flexible and serves the behavioural demands. These findings support the reinterpretation of some perspectives, such as reported biases and irrationalities in decisions, as attributes of adaptive processing towards goal fulfilment

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

Information dynamics: patterns of expectation and surprise in the perception of music

This is a postprint of an article submitted for consideration in Connection Science © 2009 [copyright Taylor & Francis]; Connection Science is available online at:http://www.tandfonline.com/openurl?genre=article&issn=0954-0091&volume=21&issue=2-3&spage=8

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

Temporal Construal Effects Are Independent of Episodic Future Thought

Human thought is prone to biases. Some biases serve as beneficial heuristics to free up limited cognitive resources or improve well-being, but their neurocognitive basis is unclear. One such bias is a tendency to construe events in the distant future in abstract, general terms and events in the near future in concrete, detailed terms. Temporal construal may rely on our capacity to orient toward and/or imagine context-rich future events. We tested 21 individuals with impaired episodic future thinking resulting from lesions to the hippocampus or ventromedial prefrontal cortex (vmPFC) and 57 control participants (aged 45-76 years) from Canada and Italy on measures sensitive to temporal construal. We found that temporal construal persisted in most patients, even those with impaired episodic future thinking, but was abolished in some vmPFC cases, possibly in relation to difficulties forming and maintaining future intentions. The results confirm the fractionation of future thinking and that parts of vmPFC might critically support our ability to flexibly conceive and orient ourselves toward future events